Using reinforcement learning to scale queue-based services

ABSTRACT

Techniques for adjusting a compute capacity of a cloud computing system. In an example, a compute scaling application accesses, from a cloud computing system, a compute capacity indicating a number of allocated compute instances of a cloud computing system and usage metrics indicating pending task requests in a queue of the cloud computing system. The compute scaling application determines, for the cloud computing system, a compute scaling adjustment by applying a machine learning model to the compute capability of the cloud computing system and the usage metrics. The compute scaling adjustment indicates an adjustment to a number of compute instances of the cloud computing system. The compute scaling application provides the compute scaling adjustment to the cloud computing system. The cloud computing system adjusts a number of allocated compute instances.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/587,906, filed on Sep. 30, 2019, now allowed, the contents of whichare incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to queue-based computing services. Morespecifically, but not by way of limitation, this disclosure involvesusing reinforcement learning to adjust a capacity of a queue-basedcomputing service in real-time.

BACKGROUND

Computing systems have become more complex, and the demands on theresources of such systems have increased. For many service providers,continuously updating computing systems to keep up with their evolvingservices is not feasible. Consequently, many service providers use cloudcomputing systems to leverage additional computing resources to assistwith providing services.

A cloud computing service system may scale a number of the allocatedprocessors or computing resources up or down to reflect current demands.But existing solutions for scaling cloud-based services require manualadjustment of parameters, especially when considering different types ofprocessing requests (e.g., applying a filter to an image versusrendering a three-dimensional object). Therefore, existing techniquesmay involve disadvantages for reasons such as (but not limited to) thosedescribed above.

SUMMARY

Techniques are disclosed herein for adjusting a number of computeinstances of a cloud computing system. In an example, a compute scalingapplication accesses, from a cloud computing system, a compute capacityindicating a number of allocated compute instances of a cloud computingsystem and usage metrics indicating pending task requests in a queue ofthe cloud computing system. The compute scaling application determines,for the cloud computing system, a compute scaling adjustment by applyinga machine learning model to the compute capability of the cloudcomputing system and the usage metrics. The compute scaling adjustmentindicates an adjustment to a number of compute instances of the cloudcomputing system. The compute scaling application provides the computescaling adjustment to the cloud computing system. The cloud computingsystem adjusts a number of allocated compute instances.

In another example, an application facilitates learning of a machinelearning model. The application accesses historical data that includes,for a point in time, a number of a compute capacity and usage metrics.The application determines a compute scaling adjustment for a cloudcomputing model by applying a machine learning model to the number of acompute capacity and the usage metrics. The compute scaling adjustmentindicates an adjustment to the number of compute instances. Theapplication modifies the number of compute instances of the cloudcomputing model according to the compute scaling adjustment. Theapplication computes a reward value as a function of an overage of themodified number of compute instances relative to a maximum number ofcompute instances, a number of pending processing requests in the queue,and a weighted sum of the modified number of compute instances relativeto the current load. The application provides the reward value to themachine learning model. The machine learning model adjusts one or moreinternal parameters to maximize a cumulative reward. Responsive todetermining that the cumulative reward is above a threshold, theapplication provides the machine learning model to a cloud computescaling system.

In another example, an application determines, for a cloud computingsystem having a number of compute instances, a compute scalingadjustment by applying a machine learning model to a compute capacityindicating a number of allocated compute instances and usage metricsindicating pending any processing requests in a queue of the cloudcomputing system. The compute scaling adjustment indicates an adjustmentto the number of compute instances. The application modifies the machinelearning model and a number of compute instances of the cloud computingsystem. Modifying the machine learning model includes computing a firstreward value and adjusting an internal parameter of the machine learningmodel. The adjusting causes a second reward value to be computed from asubsequent compute scaling adjustment. Modifying the number of computeinstances of the cloud computing system includes providing the computescaling adjustment to the cloud computing system. The cloud computingsystem allocates or deallocates more compute instances.

These illustrative embodiments are mentioned not to limit or define thedisclosure, but to provide examples to aid understanding thereof.Additional embodiments are discussed in the Detailed Description, andfurther description is provided there.

BRIEF DESCRIPTION OF THE FIGURES

Features, embodiments, and advantages of the present disclosure arebetter understood when the following Detailed Description is read withreference to the accompanying drawings.

FIG. 1 is a diagram depicting an example of a cloud computingenvironment, according to an embodiment of the present disclosure.

FIG. 2 is a flow chart depicting an example of a process for adjusting anumber of allocated compute instances of a cloud computing system,according to an embodiment of the present disclosure.

FIG. 3 is a flow chart depicting an example of a process forfacilitating learning of a machine model to adjust a number of allocatedcompute instances of a cloud computing system, according to anembodiment of the present disclosure.

FIG. 4 is a flow chart depicting an example of a process for evaluatinga reward function, according to an embodiment of the present disclosure.

FIG. 5 is a graph depicting an example of a negative feedback componentof a reward function used to calculate a reward, according to anembodiment of the present disclosure.

FIG. 6 is a graph depicting results of a simulation of cloud computingcapacity adjustments, according to an embodiment of the presentdisclosure.

FIG. 7 is a graph depicting additional results of a simulation of cloudcomputing capacity adjustments, according to an embodiment of thepresent disclosure.

FIG. 8 is a diagram depicting an example of a computing system forimplementing certain embodiments of the present disclosure.

DETAILED DESCRIPTION

Leveraging computing resources of a cloud computing system forasynchronous processing can involve allocating of computing resources onan as-needed basis. But as discussed, existing cloud computing systemsmay require adjustment of parameters when conditions change, for exampleif a pending number of computing tasks increases above a thresholdlevel. Such adjustments are cumbersome. Moreover, existing systemscannot easily adjust computing resources based on differing requirementsfor different types of computing tasks. For example, one computing taskmay take longer to execute than another task or have a stricter latencyrequirement.

In contrast, embodiments of the present disclosure can automaticallydetermine an appropriate compute scaling adjustment that can be appliedto a cloud computing system to increase or decrease a number ofallocated compute instances as appropriate as computing needs change.Examples of compute instances include, but are not limited to,predefined amounts of resources such as processor time or disk usage,virtual machines, logical processor entities, and hardware processors.This approach more efficiently uses computing resources than existingsolutions, by automatically adjusting to different types of computingtasks and load fluctuations such as seasonal or daily load fluctuations.

The following non-limiting example is introduced for discussionpurposes. A cloud scaling system monitors current computing needs versusavailable resources by using a machine learning model. The computescaling system applies the machine learning model to a compute capacityof the cloud computing system (e.g., how many compute instances arecurrently allocated and how many compute instances are available forallocation) and usage metrics (e.g., a number of pending processingrequests in a queue or a number of incoming tasks) to determine thecompute scaling adjustment. The compute scaling adjustment is providedto a cloud computing system, which increases, decreases, or maintains anumber of compute instances.

Some embodiments use reinforcement learning. Reinforcement learninginvolves motivating a machine learning model to learn to take certainactions within an environment by maximizing a cumulative reward. Thereward is determined by evaluating a reward function at differentiterations or points in time. For instance, the machine learning modelused by the cloud scaling system computes a reward based on aperformance of the cloud computing system after implementation of thecompute scaling adjustment. In contrast to existing solutions that relyon learning historic compute patterns, using reinforcement learningenables certain embodiments to learn how to make adjustments to computeinstances as parameters (e.g., queue size, pending jobs) change.

In one example, the reward function includes a sum of three components.The first component is a function of any overage or underage ofcomputing capacity subsequent to the determined adjustment in computeinstances. The first component adjusts the calculated reward downward ifa compute scaling adjustment would cause the allocated number of computeinstances in a cloud computing system to increase above a maximum numberof compute instances or fall below a minimum number of computeinstances. The second component is computed from a number of tasks inthe queue. The second component sharply decreases the outputted rewardas the number of tasks in the queue increases. The third component iscomputed from a number of allocated compute instances as compared to acurrent load, where the current load is a proportion of the allocatedcompute instances that are being used by any incoming processingrequests and the tasks in the queue. The third component indicates tothe machine learning model that reducing the number of compute instanceswhen the computing load is high is less desirable than during times whenthe computing load is low. Computing a reward from these terms canreduce unnecessary increases in number of allocated compute instanceswhile ensuring sufficient capacity exists in the cloud computing system.

Turning now to the Figures, FIG. 1 is a diagram depicting an example ofa cloud scaling system that can adjust a number of allocated computeinstances of a cloud computing system, according to an embodiment of thepresent disclosure. FIG. 1 depicts computing environment 100 having aclient computing device 110, a cloud scaling system 120, and a cloudcomputing system 150. Client computing device 110, cloud scaling system120, and cloud computing system 150 are connected via a network, whichcan be a wired network or a wireless network.

In the example provided by FIG. 1, cloud scaling system 120 accessescompute capacity 131 and usage metrics 132 from cloud computing system150 to determine a compute scaling adjustment 133. The compute scalingadjustment 133 causes cloud computing system 150 to increase or decreasea number of compute instances 170 a-n that are allocated to processcomputing tasks such as tasks generated by client computing device 110.In this manner, unneeded resources are not allocated while sufficientresources are deployed in the event of a surge in demand, for example,an increased load in the morning.

Client computing device 110 can perform any computing functions.Examples of computing functions include image processing, documentprocessing, and web browsing. In the process of performing thesefunctions, client computing device 110 can also send a request for acomputing task to be performed to cloud computing system 150. In turn,the cloud computing system 150 inserts the request (or message) intoqueue 155. The queue 155 includes a set of tasks 160 a-n. Scheduler 156allocates tasks 160 a-n from queue 155 to one or more of the computeinstances 170 a-n. Scheduler 156 can use different approaches todetermine an order in which a particular task is processed, e.g., roundrobin, first in first out, priority based, etc. Compute instances 170a-n can be virtual (e.g., a virtual machine or a maximum proportion ofresources such as processor cycles), logical (e.g., a logical core of aprocessor), or physical (e.g., a processor core or a computing system).For illustrative purposes, FIG. 1 includes an example with n tasks and ncompute instances, but the number of tasks in the queue 155 may differfrom the number of compute instances of the cloud computing system 150.

Cloud scaling system 120 can include one or more processing devices thatexecute program code and thereby determine changes in compute capacitythat should be implemented by the cloud computing system 150. Examplesof the program code executed by the cloud scaling system 120 includecloud scaling application 122, machine learning model 124, and rewardfunction 126. Cloud scaling application 122 generates a compute scalingadjustment 133 by applying machine learning model 124 to various inputs.Examples of these inputs include compute capacity 131 and usage metrics132.

In some cases, cloud scaling system 120 can be integrated with the cloudcomputing system 150. Client computing device 110 can pass processingrequests directly to cloud computing system 150. In some cases, cloudscaling system 120 can relay one or more processing requests 130 from aclient computing device to cloud computing system 150.

Cloud scaling system 120 provides the compute scaling adjustment 133 tocloud computing system 150. The cloud computing system 150 uses thereceived compute scaling adjustment 133 to modify the number ofallocated compute instances 170 a-n.

The compute capacity 131 can include one or more of a maximum ofavailable compute instances 170 a-n (e.g., that could be allocated), anda load. The load is an average of the allocated compute instances 170a-n of compute capacity that is being used. For example, if computeinstances 170 a-b are allocated, and compute instance 170 a is used at40% and compute instance 170 b at 60%, then the compute capacity 131 is50%. The usage metrics 132 can include: a number of task requests inqueue 155, a number of task requests in the queue 155 that are currentlyprocessed, or a rate at which new tasks are currently arriving.

The compute scaling adjustment 133, which is calculated by the cloudscaling system 120, is a number of compute instances 170 a-n to allocateor deallocate. For example, a compute scaling adjustment 133 of +2signifies that the number of compute instances 170 a-n should beincreased by two. An example of determining the compute scalingadjustment 133 is provided herein with respect to FIG. 2.

The machine learning model 124 can be any suitable machine-learningmodel that is configured to receive compute capacity 131 and usagemetrics 132 as inputs and determine a corresponding compute scalingadjustment 133. Examples of suitable machine learning models includemodels that can be used with reinforcement learning. Examples ofsuitable algorithms for use with the model include Proximal PolicyOptimization (PPO), Deep Q learning (DQN), Trust Region PolicyOptimization (TRPO), and Deep Determininistic Policy Gradient (DDPG)algorithms.

The machine learning model 124 can be configured via a learning process.In the learning process, one or more parameters of the machine learningmodel 124 are modified in accordance with feedback from the rewardfunction 126. Examples of determining the compute scaling adjustment 133and using the reward function 126 to modify the machine learning model124 are provided herein with respect to FIG. 3.

FIG. 2 is a flow chart depicting an example of a process for adjusting anumber of allocated compute instances of a cloud computing system,according to an embodiment of the present disclosure. Process 200 can beimplemented by cloud scaling application 122 and/or an applicationexecuting on cloud computing system 150. In some cases, only some of theoperations described in process 200 are performed.

At block 201, process 200 involves accessing, from a cloud computingsystem, a compute capacity and usage metrics. For example, cloud scalingapplication 122 accesses compute capacity 131 and usage metrics 132 fromcloud computing system.

At block 202, process 200 involves determining, for the cloud computingsystem, a compute scaling adjustment by applying a machine learningmodel to the compute capacity and the usage metrics. The compute scalingadjustment 133 indicates an adjustment to a number of allocated computeinstances 170 a-n of the cloud computing system.

Continuing the example, cloud scaling application 122 provides computecapacity 131 and usage metrics 132 to machine learning model 124. Theusage metrics 132 can indicate pending task requests in a queue of thecloud computing system 150.

At block 203, process 200 involves modifying the compute capacity byproviding the compute scaling adjustment to the cloud computing system,causing the cloud computing system to adjust a number of allocatedcompute instances. Cloud scaling application 122 can implement block 203by generating an adjustment instruction and transmitting the adjustmentinstruction to the cloud computing system 150. The adjustmentinstruction can indicate, to the cloud computing system 150, an increaseor decrease in the number of compute instances 170 a-n. In one example,the adjustment instruction includes the compute scaling adjustment. Inanother example, the cloud scaling application 122 calculates a newcompute capacity by adding the previous number of allocated computeinstances (e.g., before any compute scaling adjustment) to the computescaling adjustment calculated in block 203.

Blocks 201-203 of process 200 can be performed iteratively. Forinstance, the cloud scaling application 122 can be configured to executeblocks 203 on a periodic basis (e.g., every minute, every hour, etc.),in response to certain criteria (e.g., the queue 155 exceeding a certainsize), or some combination thereof.

In some cases, the machine learning model completes a learning processprior to being deployed in the cloud computing system, for example via asimulation of a cloud computing environment using historical usage data.FIG. 3 depicts an example of one such process.

FIG. 3 is a flow chart depicting an example of a process forfacilitating learning of a machine model to adjust a number of allocatedcompute instances of a cloud computing system, according to anembodiment of the present disclosure. As discussed, the machine learningmodel 124 can learn to adjust a compute capacity of cloud computingsystem 150 by using reinforcement learning. Process 300 describes anexample of a process for performing reinforcement learning on themachine learning model. Process 300 can be performed within a simulatedcloud computing environment or on cloud computing environment 100.

The simulated cloud computing environment simulates some or all of thecomponents depicted in FIG. 1. For example, the simulated cloudcomputing environment includes a queue, tasks, and compute instances. Inparticular, the simulated cloud computing environment permits machinelearning model 124 to use historical data that characterizes how aphysical cloud computing environment executed tasks had a queue size ofvarying lengths and a number of compute instances that was adjusted overtime. The simulated cloud computing environment can provide the computecapability and usage metrics to the machine learning model 124 such thatthe machine learning model 124 can attempt to make independentadjustments to a number of compute instances and a reward function canbe evaluated based on those adjustments. Historical data can be obtainedby monitoring tools like Splunk® or AWS CloudWatch.

At block 301, process 300 involves accessing a number of computeinstances and usage metrics for a given point in time. The historicaldata can include compute capacity and usage metrics. In some cases, thedata can also include data describing a new processing request thatarrived at the point in time. Example data for the simulation includessinusoidal load patterns, which can closely resemble day-to-night loadpattern as found on typical cloud computing services, generated fixedload that changes after a given period, or production load patterns asobtained from a cloud monitoring service.

At block 302, process 300 involves determining a compute scalingadjustment by applying a machine learning model to the averageutilization and the usage metrics. At block 302, process 300 involvessimilar operations as described with respect to block 202 of process200.

At block 303, process 300 involves evaluating the reward function todetermine a reward. The output of the machine learning model is used toevaluate the reward function (as described with respect to FIG. 4), andis provided back to the cloud computing environment to adjust thecompute instances. For instance, cloud scaling application 122implements block 303 by computing a reward value using a reward function126 and modifying the machine learning model 124 in accordance with thereward value.

At block 304, process 300 involves modifying the machine learning modelbased on the reward. Cloud scaling application 122 provides the rewardvalue to machine learning model 124. The reward function 126 is used tobalance competing considerations with respect to the cloud computingsystem 150. For instance, the feedback provided by the reward functioncan be used by the cloud scaling application 122 to generate a computescaling adjustment 133 that reduces the queue size to a desirable sizewithout increasing a compute cost (as measured by a number of activecompute instances) beyond a desirable level. For example, a desirablequeue size might be 50 tasks in the queue, whereas an undesirable queuesize may be 200.

In turn, cloud scaling application 122 can modify one or more internalparameters of the machine learning model 124 if the reward valueindicates a less desirable performance of the adjusted cloud computingsystem 150. For instance, in embodiments involving a machine learningmodel 124 that is a neural network, block 303 can involve adding orremoving one or more nodes from one or more hidden layers in the neuralnetwork, adding or removing one or more hidden layers in the neuralnetwork, etc. Additionally or alternatively, in embodiments involving amachine learning model 124 that is a tree-based machine learning model,block 303 can involve modifying the decision criteria within splittingrules. Modifying one or more internal parameters of the machine learningmodel 124 can cause a different compute scaling adjustment to becomputed in respond to a subsequent processing request. The differentcompute scaling adjustment can result in an increased reward value beingcomputed, thereby indicating an improvement in the ability of themachine learning model 124 to recommend adjustments to the cloudcomputing system 150.

At block 305, process 300 involves modifying the number of computeinstances of the cloud computing system. The reward function 126 cancompute a reward value from a set of terms representing the performanceof the cloud computing system 150, as modified in accordance with thecompute scaling adjustment. At block 305, process 300 involves similaroperations as block 203 of process 200. Block 305 can occur in parallelwith blocks 303-304.

Process 300 is iterative. For example, cloud scaling application 122computes, with the reward function 126, a reward value. In a firstiteration of the learning process, the cloud scaling application 122receives a first reward value outputted by the reward function 126. Thecloud scaling application 122 modifies one or more parameters of themachine learning model 124 to decrease the reward value in subsequentiterations. In a second iteration of the learning process, the cloudscaling application 122 receives a second reward value outputted by thereward function 126. A decrease in the reward value between theseiterations may indicate that the machine learning model 124 isgenerating compute scaling adjustments that are undesirable (e.g.,result in queues that are too large, numbers of compute instances 170a-n beyond the capabilities of the cloud scale system 120, etc.). If thesecond reward value is less than the first reward value, the cloudscaling application 122 may further modify one or more parameters of themachine learning model 124.

Additionally, by using reinforcement learning, certain embodiments areable to learn to balance short term and long term reward. For example,the machine learning model 124 can learn to accept a lower reward in thenear term if obtaining a long term reward is more beneficial. Forexample, machine learning model 124 can learn that adding new computeinstances 170 a-n can cause negative rewards because of increasedcompute costs but also positive rewards in the longer term because thenewly allocated compute instances become available and reduce the queuesize.

At block 306, process 300 involves determining whether the learning iscomplete. For example, blocks 301-305 can be repeated until a sufficientamount of learning of the machine learning model 124 has been completed.Various approaches can be used to determine that the learning iscomplete. In some cases, when a specific number of iterations (e.g.,1000) is performed, then process 300 moves to block 307. In other cases,an error between the compute scaling adjustment calculated at block 302relative to a benchmark or ideal compute scaling adjustment. When theerror is below a threshold, then process 300 continues to block 307.Otherwise, if more learning is needed then, process 300 returns to block301. At block 307, process 300 ends.

FIG. 4 is a flow chart depicting an example of a process 400 forevaluating a reward function, according to an embodiment of the presentdisclosure. Process 400 can be performed by cloud scaling application122. In this example, the reward function 126 can include a sum of threeterms, where the first term represents any scaling of a number ofcompute instances 170 a-n that is outside a predetermined set of computebounds, the second term is a negative feedback component representing anumber of tasks in the queue 155, and the third term represents a numberof allocated compute instances 170 a-n as compared to a current load.The current load is a proportion of the compute instances 170 a-n thatis being used any incoming processing requests and the tasks in thequeue 155.

For example, the reward function can be expressed as:

${R = {{- (e)} + ( {- \frac{p}{1 + p}} ) + {- ( {( {1 - \frac{load}{100}} )*( \frac{instances}{\max\mspace{14mu}{instances}} )} )}}}.$

In this example of a reward function, the R term refers to the rewardvalue, the e term refers to a penalty if the number of compute instances170 a-n is outside a predetermined set of compute bounds, the p termrefers to a number of tasks in the queue, the load term refers to acurrent load, the instances term refers to a number of compute instances170 a-n that are allocated from the total compute instances 170 a-n, andthe max instances term refers to the total compute instances 170 a-n.The terms of this reward function are described below with respect toblocks 401-404.

At block 401, process 400 involves applying a negative penalty (i.e.,the e term in the reward function above) if the compute scalingadjustment 133 generated by machine learning model 124 specifies anincrease or decrease in the number of compute instances 170 a-n to avalue that is outside a predetermined set of compute bounds. The valueof e can be set to 0.1.

A set of compute bounds could be a maximum number of compute instances,such as the largest number of compute instances that may be instantiatedusing the processing and memory capabilities of the cloud computingsystem 150, and a minimum number of compute instances, such as auser-specified value. Scaling outside this set of compute bounds couldinvolve causing the allocated number of compute instances to be greaterthan a maximum or lower than a minimum. The allocated number of computeinstances is determined by adding the previous number of allocatedcompute instances (e.g., before any compute scaling adjustment) to thecompute scaling adjustment (which may be negative if compute instancesare to be reduced).

Applying this negative penalty can decrease the reward value computed bythe reward function 126. This decrease in the reward function canindicate that a determined compute scaling adjustment 133 results in anumber of compute instances that is below the minimum or above themaximum number of compute instances. Thus, including the negativepenalty in the reward function can reduce the likelihood that themachine learning model 124 suggests a compute scaling adjustment thatwould exceed the capabilities of the cloud computing system 150 or wouldcause the number of compute instances 170 a-n to be below a minimumvalue. Examples of a minimum value are an absolute minimum, e.g., zero,and a threshold minimum number of compute instances 170 a-n that is aknown to be required to provide acceptable minimum performance.

At block 402, process 400 involves normalizing and negating a number oftasks (i.e., the p term in the reward function above) in the queue ofthe cloud computing system. Increasing the number of a number of tasksin the queue 155 can decrease a reward value computed by the rewardfunction 126, which can in turn result in the compute scaling adjustment133 having a value that causes the cloud computing system 150 toincrease the number of the compute instances 170 a-n. This number isnegated (i.e., multiplied by −1) in the reward function to represent thefact that a higher number of tasks is undesirable.

For instance, the cloud scaling application 122 obtains from the cloudcomputing system 150, usage metrics 132 that include the number of tasksin the queue 155. The cloud scaling application 122 normalizes thenumber of tasks by using an inverse odds ratio function

$\frac{p}{1 + p},$

where p is a number of tasks in the queue. This normalization maps alarge (theoretically infinite) queue size value to the range of 0 to 1.The cloud scaling application 122 negates the normalized number of tasksby multiplying the normalized number of tasks by −1. Thus, the negativefeedback component is provided by

${- \frac{p}{1 + p}}.$

An example of a set of values for this negative feedback component isdepicted in the graph 500 of FIG. 5. The graph 500 depicts an amount ofnegative feedback, or penalty, provided to the model as a function ofthe number of tasks in the queue. As can be seen, an increase in numberof tasks in the queue sharply increases the negative feedback. Theincrease in the negative feedback helps the model learn to avoid leavingtoo many tasks in the queue because the model is penalized with lowerreward when it permits the number of tasks in the queue to grow.

Returning to FIG. 4, at block 403, process 400 involves weighting thenumber of compute instances (i.e., the instances term in the rewardfunction above) by the current load (i.e., the load term in the rewardfunction above). The number of the compute instances 170 a-n, whichcould be included in or derived from the compute capacity 131, ascompared to a current load can indicate whether a current number of thecompute instances 170 a-n is sufficient to process the current load. Asa simplified example, five of ten available compute instances may beassigned, but the load is 95%. In this case, the five compute instancesmay not be able to handle load spikes in a timely manner. Increasing thevalue the “instances” term in the reward function 126 can increase areward value computed by the reward function 126, which can in turnresult in the compute scaling adjustment 133 having a value that causesthe cloud computing system 150 to increase the number of the computeinstances 170 a-n.

To implement block 403, cloud scaling application 122 weights the numberof compute instances by the current load. The current load is theproportion of the current number of compute instances that is being usedby the incoming load plus the queue size. An example of the weightingperformed at block 403 is:

${weighting}{{= {{- 1}*( {1 - \frac{load}{100}} )*( \frac{instances}{\max\mspace{14mu}{instances}} )}}.}$

As discussed above, the load term refers to a current load, theinstances term refers to a number of compute instances 170 a-n that areallocated from the total compute instances 170 a-n, the max instancesterm refers to the total compute instances 170 a-n.

In general, the model attempts to ensure that sufficient computecapacity is available, but also attempts to minimize the number ofadditional compute instances that are allocated. The weighting indicatesto the machine learning model that scaling down during times of highcomputing load is less desirable than during times of low computingloads. This weight avoids the model getting contradictory components ofthe reward function, rendering the model unable to balance queue sizeagainst cost. In an example, by maximizing cumulative reward rather thana reward for a current iteration, the model learns that while adding acompute instance might incur a small immediate penalty, the additionalcompute instance will provide additional reward in the long term byhelping avoid a penalty for incurring a longer queue size.

The sum of the three components of the reward function (as calculated byblocks 401-403 respectively) are normalized between zero and one to keepthem in balance and give the model feedback on all three areas. Once thereward value is calculated, it is provided to the machine learning model124.

FIGS. 6-7 describe examples of simulations performed to facilitatelearning of a machine learning model. FIG. 6 is a graph 600 depictingresults of a simulation of cloud computing capacity adjustments. Thegraph 600 identifies a number of incoming tasks 601, queue length 602,and a number of allocated compute instances 603, each of which vary overtime. Graph 600 can represent a simulation, e.g., a process by which amachine learning model learns to calculate a compute scaling adjustment.Graph 600 can also represent a learning process that takes place atruntime (e.g., relating to a physical cloud computing system).

The number of incoming tasks 601 can also refer to a number of incomingprocessing requests. As can be seen, the queue length 602 is constant,except for spike 611, which is caused by a number of incoming tasks 601increasing. These tasks are not immediately processed, causing spike611. As can be seen, subsequent to the queue length 602 increasing atspike 611, cloud scaling system 120 increases the number of computeinstances 603 to compensate for the spike 611. Increasing the number ofcompute instances 603 causes the queue length 602 to decrease back to aprevious level. However, the machine learning model has increased thenumber of compute instances over the maximum 610. The reward functioncan take this into consideration and cause the model not to increase thenumber of compute instances beyond this maximum 610.

FIG. 7 is a graph depicting additional results of a simulation of cloudcomputing capacity adjustments, according to an embodiment of thepresent disclosure. FIG. 7 depicts graph 700, which shows a number ofcompute instances 711 and queue length 712. As can be seen, the cloudscaling system 120 is able to react to an increase in queue length andincrease the number of compute instances. Conversely, the cloud scalingsystem 120 can decrease the number of compute instances when more areallocated than are needed.

FIG. 8 is a diagram depicting an example of a computing system forimplementing certain embodiments of the present disclosure. FIG. 8depicts computing device 800, which is an example of cloud scalingsystem 120, cloud computing system 150, or client computing device 110,and can execute cloud scaling application 122. Any suitable computingsystem may be used for performing the operations described herein. Thedepicted example of a computing device 800 includes a processor 802communicatively coupled to one or more memory devices 804. The processor802 executes computer-executable program code 830 stored in a memorydevice 804, accesses data 820 stored in the memory device 804, or both.Examples of the processor 802 include a microprocessor, anapplication-specific integrated circuit (“ASIC”), a field-programmablegate array (“FPGA”), or any other suitable processing device. Theprocessor 802 can include any number of processing devices or cores,including a single processing device. The functionality of the computingdevice may be implemented in hardware, software, firmware, or acombination thereof.

The memory device 804 includes any suitable non-transitorycomputer-readable medium for storing data, program code, or both. Acomputer-readable medium can include any electronic, optical, magnetic,or other storage device capable of providing a processor withcomputer-readable instructions or other program code. Non-limitingexamples of a computer-readable medium include a flash memory, a ROM, aRAM, an ASIC, or any other medium from which a processing device canread instructions. The instructions may include processor-specificinstructions generated by a compiler or an interpreter from code writtenin any suitable computer-programming language, including, for example,C, C++, C#, Visual Basic, Java, or scripting language.

The computing device 800 may also include a number of external orinternal devices, such as input or output devices. For example, thecomputing device 800 is shown with one or more input/output (“I/O”)interfaces 808. An I/O interface 808 can receive input from inputdevices or provide output to output devices. One or more busses 808 arealso included in the computing device 800. The bus 808 communicativelycouples one or more components of a respective one of the computingdevice 800.

The computing device 800 executes program code 830 that configures theprocessor 802 to perform one or more of the operations described herein.For example, the program code 830 causes the processor to perform theoperations described in FIGS. 1-4.

The computing device 800 also includes a network interface device 810.The network interface device 810 includes any device or group of devicessuitable for establishing a wired or wireless data connection to one ormore data networks. The network interface device 810 may be a wirelessdevice and have an antenna 814. The computing device 800 can communicatewith one or more other computing devices implementing the computingdevice or other functionality via a data network using the networkinterface device 810.

The computing device 800 can also include a display device 812. Displaydevice 812 can be a LCD, LED, touch-screen or other device operable todisplay information about the computing device 800. For example,information could include an operational status of the computing device,network status, etc.

General Considerations

Numerous specific details are set forth herein to provide a thoroughunderstanding of the claimed subject matter. However, those skilled inthe art will understand that the claimed subject matter may be practicedwithout these specific details. In other objects, methods, apparatuses,or systems that would be known by one of ordinary skill have not beendescribed in detail so as not to obscure claimed subject matter.

Unless specifically stated otherwise, it is appreciated that throughoutthis specification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining,” and “identifying” or the likerefer to actions or processes of a computing device, such as one or morecomputers or a similar electronic computing device or devices, thatmanipulate or transform data represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of thecomputing platform.

The system or systems discussed herein are not limited to any particularhardware architecture or configuration. A computing device can includeany suitable arrangement of components that provide a result conditionedon one or more inputs. Suitable computing devices include multi-purposemicroprocessor-based computer systems accessing stored software thatprograms or configures the computing system from a general purposecomputing apparatus to a specialized computing apparatus implementingone or more embodiments of the present subject matter. Any suitableprogramming, scripting, or other type of language or combinations oflanguages may be used to implement the teachings contained herein insoftware to be used in programming or configuring a computing device.

Embodiments of the methods disclosed herein may be performed in theoperation of such computing devices. The order of the blocks presentedin the examples above can be varied—for example, blocks can bere-ordered, combined, and/or broken into sub-blocks. Certain blocks orprocesses can be performed in parallel.

The use of “adapted to” or “configured to” herein is meant as open andinclusive language that does not foreclose devices adapted to orconfigured to perform additional tasks or steps. Additionally, the useof “based on” is meant to be open and inclusive, in that a process,step, calculation, or other action “based on” one or more recitedconditions or values may, in practice, be based on additional conditionsor values beyond those recited. Headings, lists, and numbering includedherein are for ease of explanation only and are not meant to belimiting.

While the present subject matter has been described in detail withrespect to specific embodiments thereof, it will be appreciated thatthose skilled in the art, upon attaining an understanding of theforegoing, may readily produce alterations to, variations of, andequivalents to such embodiments. Accordingly, it should be understoodthat the present disclosure has been presented for purposes poses ofexample rather than limitation, and does not preclude the inclusion ofsuch modifications, variations, and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.

What is claimed is:
 1. A method of managing a cloud scaling system, themethod comprising: determining, for a cloud computing system, a computescaling adjustment that indicates an adjustment to a number of computeinstances of the cloud computing system by applying a machine learningmodel to a compute capacity of the cloud computing system and usagemetrics, wherein: the compute capacity indicates a number of allocatedcompute instances, the usage metrics indicate pending task requests in aqueue of the cloud computing system, and the machine learning model istrained using reinforcement learning and a reward function that is afunction of a number of requests in the queue and the number ofallocated compute instances and comprises one or more of an overage ofthe number of compute instances relative to a maximum number of computeinstances, a number of pending processing requests in the queue, or aweighted sum of the number of compute instances relative to a currentload, wherein the current load is a proportion of a current number ofcompute instances that is used by tasks in the queue; and providing thecompute scaling adjustment to the cloud computing system, wherein thecloud computing system adjusts the number of allocated computeinstances.
 2. The method of claim 1, further comprising receiving aprocessing request from a client computing device, wherein determiningthe compute scaling adjustment further comprises applying the machinelearning model to the processing request along with the compute capacityof the cloud computing system and the usage metrics.
 3. The method ofclaim 2, further comprising removing the processing request from thequeue of the cloud computing system and executing the processingrequest.
 4. The method of claim 1, wherein adjusting the number ofcompute instances comprises allocating one or more hardware devices tothe cloud scaling system or removing the one or more hardware devicesfrom the cloud computing system.
 5. The method of claim 1, furthercomprising: computing a reward value by evaluating the reward function;and providing the reward value to the machine learning model, whereinthe machine learning model adjusts one or more internal parameters tomaximize a cumulative reward.
 6. The method of claim 5, whereincomputing the reward value by evaluating the reward function comprises:evaluating the reward function by: calculating a negative penalty basedon the compute scaling adjustment; normalizing and negating a number oftasks in the queue; and weighing the number of compute instances by acurrent load, wherein the current load is a proportion of a currentnumber of compute instances that is used by tasks in the queue; andcomputing the reward value from the negative penalty, the normalized andnegated number of tasks, and the weighted number of compute instances.7. A cloud computing system comprising: a processor; and non-transitorycomputer-readable storage medium storing computer-executable programinstructions, wherein when executed by the processor, thecomputer-executable program instructions cause the processor to performoperations comprising: determining, for a cloud computing system, acompute scaling adjustment that indicates an adjustment to a number ofcompute instances of the cloud computing system by applying a machinelearning model to a compute capacity of the cloud computing system andusage metrics, wherein: the compute capacity indicates a number ofallocated compute instances and, the usage metrics indicate pending taskrequests in a queue of the cloud computing system, and the machinelearning model is trained using reinforcement learning and a rewardfunction that is a function of a number of requests in the queue and thenumber of allocated compute instances and comprises one or more of anoverage of the number of compute instances relative to a maximum numberof compute instances, a number of pending processing requests in thequeue, or a weighted sum of the number of compute instances relative toa current load, wherein the current load is a proportion of a currentnumber of compute instances that is used by tasks in the queue; andproviding the compute scaling adjustment to the cloud computing system,wherein the cloud computing system adjusts the number of allocatedcompute instances.
 8. The cloud computing system of claim 7, whereinwhen executed by the processor, the computer-executable programinstructions further cause the processor to perform operationscomprising: receiving a processing request from a client computingdevice; and applying the machine learning model to the processingrequest along with the compute capacity of the cloud computing systemand the usage metrics.
 9. The cloud computing system of claim 7, whereinwhen executed by the processor, the computer-executable programinstructions further cause the processor to perform operationscomprising removing the processing request from the queue of the cloudcomputing system and executing the processing request.
 10. The cloudcomputing system of claim 7, wherein adjusting the number of computeinstances comprises allocating one or more hardware devices to the cloudscaling system or removing the one or more hardware devices from thecloud computing system.
 11. The cloud computing system of claim 7,wherein when executed by the processor, the computer-executable programinstructions further cause the processor to perform operationscomprising: computing a reward value by evaluating the reward function;and providing the reward value to the machine learning model, whereinthe machine learning model adjusts one or more internal parameters tomaximize a cumulative reward.
 12. The cloud computing system of claim11, wherein computing the reward value by evaluating the reward functioncomprises: evaluating the reward function by: calculating a negativepenalty based on the compute scaling adjustment; normalizing andnegating a number of tasks in the queue; and weighing the number ofcompute instances by a current load, wherein the current load is aproportion of a current number of compute instances that is used bytasks in the queue; and computing the reward value from the negativepenalty, the normalized and negated number of tasks, and the weightednumber of compute instances.
 13. A method of facilitating learning of amachine learning model, the method comprising: accessing historical datacomprising, for a point in time: a compute capacity indicating a numberof allocated compute instances; and usage metrics indicating pendingprocessing requests in a queue and a current utilization of availablecompute instances; determining a compute scaling adjustment by applyinga machine learning model to the compute capacity and to the usagemetrics, the compute scaling adjustment indicating an adjustment to anumber of compute instances; computing a reward value by: calculating anegative penalty based on the compute scaling adjustment; normalizingand negating a number of tasks in the queue, weighing the number ofcompute instances by a current load, wherein the current load is aproportion of a current number of compute instances that is used bytasks in the queue, and calculating the reward value from the negativepenalty, the normalized and negated number of tasks, and the weightednumber of compute instances; and providing the reward value to themachine learning model, wherein the machine learning model adjusts oneor more internal parameters to maximize a cumulative reward.
 14. Themethod of claim 13, further comprising applying the compute scalingadjustment to a cloud computing system to adjust the number of computeinstances.
 15. The method of claim 13, wherein the historical data isderived from a cloud computing system, the cloud computing system havingexecuted one or more processing requests.
 16. The method of claim 13,further comprising receiving a new processing request, providing the newprocessing request to the machine learning model, and determining, fromthe machine learning model, an additional compute scaling adjustment.17. The method of claim 13, wherein the historical data comprises a newprocessing request arriving at the point in time and wherein determiningthe compute scaling adjustment comprises applying the machine learningmodel to the new processing request.
 18. The method of claim 13, furthercomprising: providing the compute scaling adjustment to a cloudcomputing system; and causing the cloud computing system to executeadditional processing requests.
 19. The method of claim 13, whereincomputing the reward value further comprises normalizing and negatingnumber of processing requests in the queue.
 20. The method of claim 13,wherein computing the reward value further comprises normalizing andnegating the number of compute instances.